This paper presents the Intelligent Voice (IV) system submitted to the NIST 2016 Speaker Recognition Evaluation (SRE). The primary emphasis of SRE this year was on developing speaker recognition technology which is robust for novel languages that are much more heterogeneous than those used in the current state-of-the-art, using significantly less training data, that does not contain meta-data from those languages. The system is based on the state-of-the-art i-vector/PLDA which is developed on the fixed training condition, and the results are reported on the protocol defined on the development set of the challenge.

The irony, of course, is that this year the computer security industry, with $75 billion in annual revenue, has started to talk about how machine learning and pattern recognition techniques will improve the woeful state of computer security. "The thing people don't get is that cybercrime is becoming automated and it is scaling exponentially," said Marc Goodman, a law enforcement agency adviser and the author of Future Crimes. He added, "This is not about Matthew Broderick hacking from his basement," a reference to the 1983 movie War Games.

The text is supported by a strong outline. The authors preserve much of the introductory material, but add the latest techniques and developments in data mining, thus making this a comprehensive resource for both beginners and practitioners. The focus is data-all aspects. The presentation is broad, encyclopedic, and comprehensive, with ample references for interested readers to pursue in-depth research on any technique. "This interesting and comprehensive introduction to data mining emphasizes the interest in multidimensional data mining--the integration of online analytical processing (OLAP) and data mining. Some chapters cover basic methods, and others focus on advanced techniques. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers."

Austria based ANYLINE, the leading OCR Optical Character Recognition) technology provider for mobile devices, is solely focused on fast and accurate text recognition. Whether reading text across a room, a license plate on a car, or a gas meter in a manufacturing plant, the ANYLINE technology is able to deliver a fast and robust alternative to inputting data via voice recognition, typing, or button scrolling. This type of data import is still difficult to accomplish, as it requires a higher processor power and camera resolution. ANYLINE will now leverage the ... Read more

If you've looked into buying smart devices for your home, you've probably seen the "Works with Nest" badge printed on the outside of one of the boxes, considering how many devices are a part of the service. Alphabet has been pushing to make its Nest thermostat the center of everyone's home by getting IoT manufacturers into the program. Thanks to Alphabet's research in machine learning and pattern recognition, the Works with Nest program has gained extra smarts, allowing your other devices to hook into, and react to, more events. Benefitting most from the newfound capabilities are the Nest cameras, including the new Nest Cam Outdoor. Thanks to Alphabet being able to train its image processing on the millions of photos uploaded by Google Photos' 200 million users, the cameras have gained the ability to recognize if the movement it sees in frame is an actual person or something like a car driving by.

Neurensic, a Chicago-based regtech artificial intelligence (AI) startup, has announced the release of its new SCORE surveillance platform, the trading industry's first compliance solution powered by a cloud-based machine learning architecture which is able to identify complex patterns of trading behavior on a massive scale, across multiple markets in near real time. The FM London Summit is almost here. The development of the new SCORE platform was led by David Widerhorn and Neurensic's CTO, Dr Cliff Click, the inventor of the H2O artificial intelligence framework, the world's fastest distributed machine learning architecture. SCORE combines high-speed, big data processing power with self-adaptive pattern recognition technology, providing firms with a continuous assessment of the compliance risk associated with complex trading behaviours. The firm's recently completed beta release provided clients with surveillance technology for regulators, proprietary trading firms and futures commission merchants, culminating in engagements with larger institutional customers, including broker-dealers and global banks.

DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance. In DNN-based image recognition, computers can extract and learn the characteristics of objects on their own, thus significantly improving the accuracy of detection and identification of a wide range of objects. Because of the rapid progress in DNN technology, the two companies plan to make the technology flexibly extendable to various network configurations.

DENSO Corporation and Toshiba Corporation have reached a basic agreement to jointly develop an artificial intelligence technology called Deep Neural Network-Intellectual Property (DNN-IP), which will be used in image recognition systems which have been independently developed by the two companies to help achieve advanced driver assistance and automated driving technologies. This Smart News Release features multimedia. DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance.

Following on from an earlier story, where I introduced some common patterns used to build chat bots, we're now going to look at building some of those patterns using IBM Watson. If you haven't used the Watson Conversation service before, you may want to read about the basics of building a bot with Watson in "Getting Chatty with IBM Watson". One of the things I talked about was providing guidance at the beginning of the chat. To provide this before the user says anything you can add a "conversation_start" condition to a node. You can add more conditions to "conversation_start" nodes if you want to have different introductions depending on some external factor, e.g. from your app you could pass in the time of day in the context, and then say "good morning", "good afternoon" or "good evening" depending on that value.

KARIYA, Japan & TOKYO--(BUSINESS WIRE)--DENSO Corporation and Toshiba Corporation have reached a basic agreement to jointly develop an artificial intelligence technology called Deep Neural Network-Intellectual Property (DNN-IP), which will be used in image recognition systems which have been independently developed by the two companies to help achieve advanced driver assistance and automated driving technologies. DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To achieve automated driving, automotive computers need to be able to identify different road traffic situations including a variety of obstacles and road markings, availability of road space for driving, and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance. In DNN-based image recognition, computers can extract and learn the characteristics of objects on their own, thus significantly improving the accuracy of detection and identification of a wide range of objects.